Back it on up…

A quick refresher from last time: I pulled data from 50 keyword-targeted articles written on Brafton’s blog between January and June of 2018.

We used a technique of writing these articles published earlier on Moz that generates some seriously awesome results (we’re talking more than doubling our organic traffic in the last six months, but we will get to that in another publication).

We pulled this data again… Only I updated and reran all the data manually, doubling the dataset. No APIs. My brain is Swiss cheese.

We wanted to see how newly written, original content performs over time, and which factors may have impacted that performance.

Why do this the hard way, dude?

“Why not just pull hundreds (or thousands!) of data points from search results to broaden your dataset?”, you might be thinking. It’s been done successfully quite a few times!

Trust me, I was thinking the same thing while weeping tears into my keyboard.

The answer was simple: I wanted to do something different from the massive aggregate studies. I wanted a level of control over as many potentially influential variables as possible.

By using our own data, the study benefited from:

The same root Domain Authority across all content.

Similar individual URL link profiles (some laughs on that later).

Known original publish dates and without reoptimization efforts or tinkering.

Known original keyword targets for each blog (rather than guessing).

Known and consistent content depth/quality scores (MarketMuse).

Similar content writing techniques for targeting specific keywords for each blog.

You will never eliminate the possibility of misinterpreting correlation as causation. But controlling some of the variables can help.

What we gained in control, we lost in sample size. A sample size of 96 is much less useful than ten thousand, or a hundred thousand. So look at the data carefully and use discretion when considering the ranking factors you find most likely to be true.

This resource can help gauge the confidence you should put into each Pearson Correlation value. Generally, the stronger the relationship, the smaller sample size needed to be be confident in the results.

So what exactly have you done here?

We have generated hints at what may influence the organic performance of newly created content. No more, and no less. But they are indeed interesting hints and maybe worth further discussion or research.

What have you not done?

We have not published sweeping generalizations about Google’s algorithm. This post should not be read as a definitive guide to Google’s algorithm, nor should you assume that your site will demonstrate the same correlations.

So what should I do with this data?

The best way to read this article, is to observe the potential correlations we observed with our data and consider the possibility of how those correlations may or may not apply to your content and strategy.

I’m hoping that this study takes a new approach to studying individual URLs and stimulates constructive debate and conversation.

The analysis

1. Time and performance

I started with a question: “Do blogs age like a Macallan 18 served up neat on a warm summer Friday afternoon, or like tepid milk on a hot summer Tuesday?”

Does the time indexed play a role in how a piece of content performs?

Correlation 1: Time and target keyword position

First we will map the target keyword ranking positions against the number of days its corresponding blog has been indexed. Visually, if there is any correlation we will see some sort of negative or positive linear relationship.

There is a clear negative relationship between the two variables, which means the two variables may be related. But we need to go beyond visuals and use the PCC.

Days live vs. target keyword position

PCC

-.343

Relationship

Moderate

The data shows a moderate relationship between how long a blog has been indexed and the positional ranking of the target keyword.

But before getting carried away, we shouldn’t solely trust one statistical method and call it a day. Let’s take a look at things another way: Let’s compare the average age of articles whose target keywords rank in the top ten against the average age of articles whose target keywords rank outside the top ten.

Average age of articles based on position

Target KW position ≤ 10

144.8 days

Target KW position > 10

84.1 days

Now a story is starting to become clear: Our newly written content takes a significant amount of time to fully mature.

But for the sake of exhausting this hint, let’s look at the data one final way. We will group the data into buckets of target keyword positions, and days indexed, then apply them to a heatmap.

This should show us a clear visual clustering of how articles perform over time.

This chart, quite literally, paints a picture. According to the data, we shouldn’t expect a new article to realize its full potential until at least 100 days, and likely longer. As a blog post ages, it appears to gain more favorable target keyword positioning.

Correlation 2: Time and total ranking keywords on URL

You’ll find that when you write an article it will (hopefully) rank for the keyword you target. But often times it will also rank for other keywords. Some of these are variants of the target keyword, some are tangentially related, and some are purely random noise.

Instinct will tell you that you want your articles to rank for as many keywords as possible (ideally variants and tangentially related keywords).

Predictably, we have found that the relationship between the number of keywords an article ranks for and its estimated monthly organic traffic (per SEMrush) is strong (.447).

We want all of our articles to do things like this:

We want lots of variants each with significant search volume. But, does an article increase the total number of keywords it ranks for over time? Let’s take a look.

Visually this graph looks a little murky due to the existence of two clear outliers on the far right. We will first run the analysis with the outliers, and again without. With the outliers, we observe the following:

Days live vs. total keywords ranking on URL (w/outliers)

PCC

.281

Relationship

Weak/borderline moderate

There appears to be a relationship between the two variables, but it isn’t as strong. Let’s see what happens when we remove those two outliers:

Visually, the relationship looks stronger. Let’s look at the PCC:

Days live vs. total keywords ranking on URL (without outliers)

PCC

.390

Relationship

Moderate/borderline strong

The relationship appears to be much stronger with the two outliers removed.

But again, let’s look at things another way.

Let’s look at the average age of the top 25% of articles and compare them to the average age of the bottom 25% of articles:

Average age of top 25% of articles versus bottom 25%

Top 25%

148.9 days

Bottom 25%

73.8 days

This is exactly why we look at data multiple ways! The top 25% of blog posts with the most ranking keywords have been indexed an average of 149 days, while the bottom 25% have been indexed 74 days — roughly half.

To be fully sure, let’s again cluster the data into a heatmap to observe where performance falls on the time continuum:

We see a very similar pattern as in our previous analysis: a clustering of top-performing blogs starting at around 100 days.

Time and performance assumptions

You still with me? Good, because we are saying something BIG here. In our observation, it takes between 3 and 5 months for new content to perform in organic search. Or at the very least, mature.

To look at this one final way, I’ve created a scatterplot of only the top 25% of highest performing blogs and compared them to their time indexed:

There are 48 data plots on this chart, the blue plots represent the top 25% of articles in terms of strongest target keyword ranking position. The orange plots represent the top 25% of articles with the highest number of keyword rankings on their URL. (These can be, and some are, the same URL.)

Looking at the data a little more closely, we see the following:

90% of the top 25% of highest-performing content took at least 100 days to mature, and only two articles took less than 75 days.

Time and performance conclusion

For those of you just starting a content marketing program, remember that you may not see the full organic potential for your first piece of content until month 3 at the earliest. And, it takes at least a couple months of content production to make a true impact, so you really should wait a minimum of 6 months to look for any sort of results.

In conclusion, we expect new content to take at least 100 days to fully mature.

2. Links

But wait, some of you may be saying. What about links, buddy? Articles build links over time, too!

It stands to reason that, over time, a blog will gain links (and ranking potential) over time. Links matter, and higher positioned rankings gain links at a faster rate. Thus, we are at risk of misinterpreting correlation for causation if we don’t look at this carefully.

But what none of you know, that I know, is that being the terrible SEO that I am, I had no linking strategy with this campaign.

And I mean zero strategy. The average article generated 1.3 links from .5 linking domains.

Nice.

Linking domains vs. target keyword position

PCC

-.022

Relationship

None

Average linking domains to top 25% of articles

.46

Average linking domains to bottom 25% of articles

.46

The one thing consistent across all the articles was a shocking and embarrassing lack of inbound links. This is demonstrated by an insignificant correlation coefficient of -.022. The same goes for the total number of links per URL, with a correlation coefficient of -.029.

These articles appear to have performed primarily on their content rather than inbound links.

(And they certainly would have performed much better with a strong, or any, linking strategy. Nobody is arguing the value of links here.) But mostly…

Shame on me.

Shame. Shame. Shame.

But on a positive note, we were able to generate a more controlled experiment on the effects of time and blog performance. So, don’t fire me just yet?

Note: It would be interesting to pull link quality metrics into the discussion (for the precious few links we did earn) rather than total volume. However, after a cursory look at the data, nothing stood out as being significant.

3. Word count

Content marketers and SEOs love talking about word count. And for good reason. When we collectively agreed that “quality content” was the key to rankings, it would stand to reason that longer content would be more comprehensive, and thus do a better job of satisfying searcher intent. So let’s test that theory.

Correlation 1: Target keyword position versus total word count

Will longer articles increase the likelihood of ranking for the keyword you are targeting?

Not in our case. To be sure, let’s run a similar analysis as before.

Word count vs. target keyword position

PCC

.111

Relationship

Negligible

Average word count of top 25% articles

1,774

Average word count of bottom 25% articles

1,919

The data shows no impact on rankings based on the length of our articles.

Correlation 2: Total keywords ranking on URL versus word count

One would think that longer content would result in is additional ranking keywords, right? Even by accident, you would think that the more related topics you discuss in an article, the more keywords you will rank for. Let’s see if that’s true:

Total keywords ranking on URL vs. word count

PCC

-.074

Relationship

None

Not in this case.

Word count, speculative tangent

So how can it be that so many studies demonstrate higher word counts result in more favorable rankings? Some reconciliation is in order, so allow me to speculate on what I think may be happening in these studies.

Most likely: Measurement techniques. These studies generally look at one factor relative to rankings: average absolute word count based on position. (And, there actually isn’t much of a difference in average word count between position one and ten.)

As we are demonstrating in this article, there may be many other factors at play that need to be isolated and tested for correlations in order to get the full picture, such as: time indexed, on-page SEO (to be discussed later), Domain Authority, link profile, and depth/quality of content (also to be discussed later with MarketMuse as a measure). It’s possible that correlation does not imply correlation, and by using word count averages as the single method of measure, we may be painting too broad of a stroke.

Likely: High quality content is longer, by nature. We know that “quality content” is discussed in terms of how well a piece satisfies the intent of the reader. In an ideal scenario, you will create content that fully satisfies everything a searcher would want to know about a given topic. Ideally you own the resource center for the topic, and the searcher does not need to revisit SERPs and weave together answers from multiple sources. By nature, this type of comprehensive content is quite lengthy. Long-form content is arguably a byproduct of creating for quality. Cyrus Shepard does a better job of explaining this likelihood here.

Less likely: Long-form threshold. The articles we wrote for this study ranged from just under 1,000 words to nearly as high as 4,000 words. One could consider all of these as “long-form content,” and perhaps Google does as well. Perhaps there is a word count threshold that Google uses.

This is all speculation. What we can say for certain is that all our content is 900 words and up, and shows no incremental benefit to be had from additional length.

Feel free to disagree with any (or all) of my speculations on my interpretation of the discrepancies of results, but I tend to have the same opinion as Brian Dean with the information available.

4. MarketMuse

At this point, most of you are familiar with MarketMuse. They have created a number of AI-powered tools that help with content planning and optimization.

We use the Content Optimizer tool, which evaluates the top 20 results for any keyword and generates an outline of all the major topics being discussed in SERPs. This helps you create content that is more comprehensive than your competitors, which can lead to better performance in search.

Based on the competitive landscape, the tool will generate a recommended content score (their proprietary algorithm) that you should hit in order to compete with the competing pages ranking in SERPs.

But… if you’re a competitive fellow, what happens if you want to blow the recommended score out of the water? Do higher scores have an impact on rankings? Does it make a difference if your competition has a very low average score?

We pulled every article’s content score, along with MarketMuse’s recommended scores and the average competitor scores, to answer these questions.

Correlation 1: Overall MarketMuse content score

Does a higher overall content score result in better rankings? Let’s take a look:

Absolute MarketMuse score vs. target keyword position

PCC

.000

Relationship

None

A perfect zero! We weren’t able to beat the system by racking up points. I also checked to see if a higher absolute score would result in a larger number of keywords ranking on the URL — it doesn’t.

Correlation 2: Beating the recommended score

As mentioned, based on the competitive landscape, MarketMuse will generate a recommended content score. What happens if you blow the recommended score out of the water? Do you get bonus points?

In order to calculate this correlation, we pulled the content score percentage attainment and compared it to the target keyword position. For example, if we scored a 30 of recommended 25, we hit 120% attainment. Let’s see if it matters:

Percentage content score attainment vs. target keyword position

PCC

.028

Relationship

None

No bonus points for doing extra credit!

Correlation 3: Beating the average competitors’ scores

Okay, if you beat MarketMuse’s recommendations, you don’t get any added benefit, but what if you completely destroy your competitors’ average content scores?

We will calculate this correlation the same way we previously did, with percentage attainment over the average competitor. For example, if we scored a 30 over the average of 10, we hit 300% attainment. Let’s see if that matters:

Percentage attainment over average competitor score versus target KW position

PCC

-.043

Relationship

None

That didn’t work either! Seems that there are no hacks or shortcuts here.

MarketMuse summary

We know that MarketMuse works, but it seems that there are no additional tricks to this tool.

If you regularly hit the recommended score as we did (average 110% attainment, with 81% of blogs hitting 100% attainment or better) and cover the topics prescribed, you should do well. But don’t fixate on competitor scores or blowing the recommended score out of the water. You may just be wasting your time.

Note: It’s worth noting that we probably would have shown stronger correlations had we intentionally bombed a few MarketMuse scores. Perhaps a test for another day.

5. On-page optimization

Ah, old-school technical SEO. This type of work warms the cockles of a seasoned SEO’s heart. But does it still have a place in our constantly evolving world? Has Google advanced to the point where it doesn’t need technical cues from SEOs to understand what a page is about?

To find out, I have pulled Moz’s on-page optimization score for every article and compared them to the target keywords’ positional rankings:

Let’s take a look at the scatterplot for all the keyword targets.

Now looking at the math:

On-page optimization score vs. target keyword position

PCC

-.384

Relationship

Moderate/strong

Average on-page score for top 25%

91%

Average on-page score for bottom 25%

87%

If you have a keen eye you may have noticed a few strong outliers on the scatterplot. If we remove three of the largest outliers, the correlation goes up to -.435, a strong relationship.

Before we jump to conclusions, let’s look at this data one final way.

Let’s take a look at the percentage of articles with their target keywords ranking 1–10 that also have a 90% on-page score or better. We will compare that number to the percentage of articles ranking outside the top ten that also have a 90% on-page score or better.

If our assumption is correct, we will see a much higher percentage of keywords ranking 1–10 with an on-page score of 90% or better, and a lower number for articles ranking greater than 10.

On-page optimization score by rankings

Percentage of KWs ranking 1–10 with ≥ 90% score

73.5%

Percentage of keywords ranking >10 with ≥ 90% score

53.2%

This is enough of a hint for me. I’m implementing a 90% minimum on-page score from here on out.

Old school SEOs, rejoice!

6. The competition’s average word count

We won’t put this “word count” argument to bed just yet…

Let’s ask ourselves, “Does it matter how long the average content of the top 20 results is?”

Is there a relationship between the length of your content versus the average competitor?

What if your competitors are writing very short form, and you want to beat them with long-form content?

We will measure this the same way as before, with percentage attainment. For example, if the average word count of the top 20 results for “content marketing agency” is 300, and our piece is 450 words, we hit 150% attainment.

Let’s see if you can “out-verbose” your opponents.

Percentage word count attainment versus target KW position

PCC

.062

Relationship

None

Alright, I’ll put word count to bed now, I promise.

7. Keyword density

You’ve made it to the last analysis. Congratulations! How many cups of coffee have you consumed? No judgment; this report was responsible for entire coffee farms being completely decimated by yours truly.

For selfish reasons, I couldn’t resist the temptation to dispel this ancient tactic of “using target keywords” in blog content. You know what I’m talking about: when someone says “This blog doesn’t FEEL optimized… did you use the target keyword enough?”

There are still far too many people that believe that littering target keywords throughout a piece of content will yield results. And misguided SEO agencies, along with certain SEO tools, perpetuate this belief.

Yoast has a tool in WordPress that some digital marketers live and die by. They don’t think that a blog is complete until Yoast shows the magical green light, indicating that the content has satisfied the majority of its SEO recommendations:

Uh oh, keyword density is too low! Let’s see if it that ACTUALLY matters.

Not looking so good, my keyword-stuffing friends! Let’s take a look at the PCC:

Target keyword ranking position vs. Yoast keyword density

PCC

.097

Relationship

None/Negligible

Believers would like to see a negative relationship here; as the keyword density goes down, the ranking position decreases, producing a downward sloping line.

What we are looking at is a slightly upward-sloping line, which would indicate losing rankings by keyword stuffing — but fortunately not TOO upward sloping, given the low correlation value.

Okay, so PLEASE let that be the end of “keyword density.” This practice has been disproven in past studies, as referenced by Zyppy. Let’s confidently put this to bed, forever. Please.

Oh, and just for kicks, the Flesch Reading Ease score has no bearing on rankings either (-.03 correlation). Write to a third grade level, or a college level, it doesn’t matter.

TL;DR (I don’t blame you)

What we learned from our data

Time: It took 100 days or more for an article to fully mature and show its true potential. A content marketing program probably shouldn’t be fully scrutinized until month 5 or 6 at the very earliest.

Links: Links matter, I’m just terrible at generating them. Shame.

Word count: It’s not about the length of the content, in absolute terms or relative to the competition. It’s about what is written and how resourceful it is.

MarketMuse: We have proven that MarketMuse works as it prescribes, but there is no added benefit to breaking records.

On-page SEO: Our data demonstrates that it still matters. We all still have a job.

Competitor content length: We weren’t successful at blowing our competitors out of the water with longer content.

Keyword density: Just stop. Join us in modern times. The water is warm.

In conclusion, some reasonable guidance we agree on is:

Wait at least 100 days to evaluate the performance of your content marketing program, write comprehensive content, and make sure your on-page SEO score is 90%+.

Oh, and build links. Unlike me. Shame.

Now go take a nap.

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The latest Wolfgang E-Commerce Report is now live. This study gives a comprehensive view of the state of digital marketing in retail and travel, allowing digital marketers to benchmark their 2018 performance and plan their 2019 strategy.

The study analyzes over 250 million website sessions and more than €500 million in online revenue. Google Analytics, new Facebook Analytics reports, and online surveys are used to glean insights.

Revenue volume correlations

One of the unique features of the study is its conversion correlation. All website metrics featured in the study are correlated with conversion success to reveal what the most successful websites do differently.

This year we’ve uncovered our strongest success correlation ever at 0.67! Just to give that figure context: normally, 0.2 is worth talking about and 0.3 is noteworthy. Not only is this correlation with success very strong, the insight itself is highly actionable and can become a pillar of your digital marketing strategy.

And the stand out metric is (drumroll, please!)…

Number of sessions per user.

To put it plainly, the websites that generate the most online revenue have the highest number of sessions per user over 12 months. Check out the video below to get a detailed explanation of this phenomenon:

These are the top factors that correlated with revenue volume. You can see the other correlations in the full study.

Click to see a bigger version

Average pages per session (.37)

Average session length (.49)

Conversion rate by users (.41)

Number of sessions per user (.67)

Percentage of sessions from paid search (.25)

Average website engagement metrics

Number of sessions per user

Average pages per session

Average session duration

Bounce rate

Average page load time

Average server response time

Retail

1.58

6

3min 18sec

38.04%

6.84

1.02

Multi-channel

1.51

6

3min 17sec

35.27%

6.83

1.08

Online-only

1.52

5

3min 14sec

43.80%

6.84

0.89

Travel

1.57

3

2min 34sec

44.14%

6.76

0.94

Overall

1.58

5

3min 1sec

41.26%

6.80

0.97

Above are the average website engagement metrics. You can see the average number of sessions per user is very low at 1.5 over 12 months. Anything a digital marketer can do to get this to 2, to 3, and to 4 makes for about the best digital marketing they can do.

At Wolfgang Digital, we’ve been witnessing this phenomenon at a micro-level for some time now. Many of our most successful campaigns of late have been focused on presenting the user with an evolving message which matures with each interaction across multiple media touchpoints.

What’s the average conversion rate for online-only vs multi-channel retailers?

What’s the average order value for a hotel vs. tour operator?

Video Transcript

Today I want to talk to you about the most important online consumer trend in 2018. The story starts in a client meeting about four years ago, and we were meeting with a travel client. We got into a discussion about bounce rate and its implication on conversion rate. The client was asking us, “could we optimize our search and social campaigns to reduce bounce rate?”, which is a perfectly valid question.

But we were wondering: Will we lower the rate of conversions? Are all bounces bad? As a result of this meeting, we said, “You know, we need a really scientific answer to that question about any of the website engagement metrics or any of the website channels and their influence on conversion.” Out of that conversation, our E-Commerce KPI Report was born. We’re now four years into it. (See previous years on the Moz Blog: 2015, 2016, 2017.)

The metric with the strongest correlation to conversions: Number of sessions per user

We’ve just released the 2019 E-Commerce KPI Report, and we have a standout finding, probably the strongest correlation we’ve ever seen between a website engagement metric and a website conversion metric. This is beautiful because we’re all always optimizing for conversion metrics. But if you can isolate the engagement metrics which deliver, which are the money-making metrics, then you can be much more intelligent about how you create digital marketing campaigns.

The strongest correlation we’ve ever seen in this study is number of sessions per user, and the metric simply tells us on average how many times did your users visit your website. What we’re learning here is any digital marketing you can do which makes that number increase is going to dramatically increase your conversions, your revenue success.

Change the focus of your campaigns

It’s a beautiful metric to plan campaigns with because it changes the focus. We’re not looking for a campaign that’s a one-click wonder campaign. We’re not looking for a campaign that it’s one message delivered multiple times to the same user. Much more so, we’re trying to create a journey, multiple touchpoints which deliver a user from their initial interaction through the purchase funnel, right through to conversion.

Create an itinerary of touchpoints along the searcher’s journey

1. Research via Google

Let me give you an example. We started this with a story about a travel company. I’m just back from a swimming holiday in the west of Ireland. So let’s say I have a fictional travel company. We’ll call them Wolfgang Wild Swimming. I’m going to be a person who’s researching a swimming holiday. So I’m going to go to Google first, and I’m going to search for swimming holidays in Ireland.

2. E-book download via remarketing

I’m going to go to the Wolfgang Wild Swimming web page, where I’m going to read a little bit about their offering. In doing that, I’m going to enter their Facebook audience. The next time I go to Facebook, they’re now remarketing to me, and they’ll be encouraging me to download their e-book, which is a guide to the best swimming spots in the wild west of Ireland. I’m going to volunteer my email to them to get access to the book. Then I’m going to spend a bit more time consuming their content and reading their book.

3. Email about a local offline event

A week later, I get an email from them, and they’re having an event in my area. They’re going for a swim in Dublin, one of my local spots in The Forty Foot, for example. I’m saying, “Well, I was going to go for a swim this weekend anyway. I might as well go with this group.” I go to the swim where I can meet the tour guides. I can meet people who have been on it before. I’m now really close to making a purchase.

4. YouTube video content consumed via remarketing

Again, a week later, they have my email address, so they’re targeting me on YouTube with videos of previous holidays. Now I’m watching video content. All of a sudden, Wolfgang Wild Swimming comes up. I’m now watching a video of a previous holiday, and I’m recognizing the instructors and the participants in the previous holidays. I’m really, really close to pressing Purchase on a holiday here. I’m on the phone to my friend saying, “I found the one. Let’s book this.”

Each interaction moves the consumer closer to purchase

I hope what you’re seeing there is with each interaction, the Google search, the Facebook ad which led to an e-book download, the offline event, back online to the YouTube video, with each interaction I’m getting closer to the purchase.

You can imagine the conversion rate and the return on ad spend on each interaction increasing as we go. This is a really powerful message for us as digital marketers. When we’re planning a campaign, we think about ourselves as though we’re in the travel business too, and we’re actually creating an itinerary. We’re simply trying to create an itinerary of touchpoints that guide a searcher through awareness, interest, right through to action and making that purchase.

I think it’s not just our study that tells us this is the truth. A lot of the best-performing campaigns we’ve been running we’ve seen this anecdotally, that every extra touchpoint increases the conversion rate. Really powerful insight, really useful for digital marketers when planning campaigns. This is just one of the many insights from our E-Commerce KPI Report. If you found that interesting, I’d urge you to go read the full report today.

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Summary

We helped Repux generate 253% more leads, nearly 100% more token sales and millions of dollars in incremental revenue during their initial coin offering (ICO) by using our CRO expertise.

The optimized site also helped them get meetings with some of the biggest names in the venture capital community — a big feat for a Poland-based team without the pedigree typically required (no MIT, Stanford, Ivy League, Google, Facebook, Amazon, Microsoft background).

The details:

Repux is a marketplace that lets small and medium businesses sell anonymized data to developers. The developers use the data to build “artificially intelligent” apps, which they then sell back to businesses. Business owners and managers use the apps to make better business decisions.

Below is the original page, which linked to a dense whitepaper. We don’t know who decided that an ICO requires a long, dry whitepaper, but this seems to be the norm!

This page above suffers from several issues:

The headline is pretty meaningless (“Decentralized Data & Applications Protocol for SMEs). Remember, as David Ogilvy noted, 90% of the success of an ad (in our case, a landing page) is determined by the headline. Visitors quickly scan the headline and if it doesn’t hold their interest, bounce immediately. With so much content on the web, attention is scarce — the average time spent on a page is a few seconds and the average bounce rate is about 85%.

The call to action is “Get Whitelisted,” which is also meaningless. What’s in it for me? Why should I want to “Get Whitelisted”?

A lack of urgency to act. There is a compelling reason to do so, but it was not being clearly articulated (“Get 50% OFF on the tokens if you buy before a certain date.”)

Too much jargon and arcane technical language: Our research using Mouseflow’s on-page feedback feature showed that the non-accredited-investor ICO audience isn’t sophisticated. They typically reside outside of the US and have a limited command of English. Most are younger men (18–35) who made money from speculative activities on the Internet (affiliate marketing, Adsense arbitrage, and of course other crypto-currencies). When we surveyed them, many did not initially understand the concept. In our winning page (below), we dumbed down things a lot!

Below is the new page that produced a 253% gain in leads (email opt-ins). Coupled with the email follow-up sequence shown below, it produced a nearly 100% gain in token sales.

Winning page (above the fold):

Here are few of the elements that we believe made a difference:

Much clearer headline (which we improved upon further in a subsequent treatment).

Simple explanation of what the company is doing

Urgency to buy now — get 50% off on tokens if you buy before the countdown timer expires

Solicited and used press mentions

Social proof from the Economist; tapping a meme can be powerful as it’s always easier to swim downstream than upstream. “Data is the new oil” is a current meme.

More persuasive elements (below the fold):

In the second span (the next screenful below the fold) we added a few more persuasive elements.

For one, we highlighted key Repux accomplishments and included bios of two advisors who are well known in the crypto-community.

Having a working platform was an important differentiator because only one in 10 ICOs had a working product. Most launched with just a whitepaper!

A survey of the token buyers showed that mentioning well-known advisors worked — several respondents said it was the decisive factor in persuading them to buy. Before, the advisors were buried in a little-visited page. We featured them more prominently.

Interestingly, this seemed to cut both ways. One of the non-contributors said he was initially interested because of a certain advisor’s involvement. He later chose not to contribute because he felt this advisor’s other flagship project had been mismanaged!

We also used 3 concrete examples to show how the marketplace functions and how the tokens would be used:

When your product is highly abstract and technical, using concrete examples aids understanding. We also found this to be true when pitching to professional investors. They often asked, “Can you give me an example of how this would work in the real world?”

We like long-form pages because unlike a live selling situation, there’s no opportunity for a back-and-forth conversation. The page must therefore overcorrect and address every objection a web visitor might have.

Lastly, we explained why Repux is likely to succeed. We quoted Victor Hugo for good measure, to create an air of inevitability:

How much impact did Victor Hugo have? I don’t know, but the page did much better overall. Our experience shows that radical redesigns (that change many page elements at the same time) produce higher conversion lifts.

Once you attain a large lift, if you like, you can then do isolation testing of specific variables to determine how much each change contributed.

13% lift: Simplified alternate page

The page below led to a further 13% lift.

The key elements we changed were:

Simplified the headline even further: “Repux Monetizes Data from Millions of Small Enterprises.” What was previously the headline is now stated in the bullet points.

Added a “5 Reasons Why Repux is Likely to Succeed” section: When you number things, visitors are more likely to engage with the content. They may not read all the text but will at least skim over the numbered sub-headlines to learn what all the points are — just like power abhors a vacuum, the mind can’t seem to stand incompleteness!

We’ve seen this in Mouseflow heatmaps. You can do this test yourself: List a bunch of bullet points versus a numbered list and with a compelling headline: The 7 Reasons Why 20,0000 Doctors Recommend Product X or The 3 Key Things You Need to Know to Make an Informed Decision.

Follow-up email sequence

We also created a follow-up email sequence for Repux that led to more token sales.

As you can see, the average open rate is north of 40%, and the goal attained (token sales) is above 8%. According to Mailchimp, the average email marketing campaign open rate is about 20%, while the average CTR is about 3%.

They’re educational (versus pure sales pitch). This is also important to avoid “burning out” your list. If all you do is send pitch after pitch, soon you’ll be lucky to get a 1.3% open rate!

They employ storytelling. We use a technique known as the “Soap Opera Sequence.” Each email creates anticipation for the next one and also refers to some interesting fact in previous ones. If a person would only have opened one email, they are now likely to want to open future ones as well as look up older ones to “solve the puzzle.” This leads to higher open rates for the entire sequence, and more sales.

The calls to action are closer to the bottom, having first built up some value. Counterintuitively, this works better, but you should always test radically different approaches.

Email is a massively underutilized medium. Most businesses are sitting on goldmines (their email list) without realizing it! You can — and should — make at least 2x to 3x as many sales from your email list as you do from direct website sales.

It takes a lot of work to write an effective sequence, but once you do you can run it on autopilot for years, making money hand over fist. As customer acquisition gets ever more competitive and expensive, how well you monetize your list can make the difference between success and failure.

Conclusion

To increase the conversion rate on your website and get more sales, leads, or app downloads, follow these simple steps:

Put in the work to understand why the non-converting visitors are leaving and then systematically address their specific objections. This is what “research-driven” optimization means, as opposed to redesign based purely aesthetic appeal or “best practices.”

Find out why the converting visitors took the desired action — and then accentuate these things.

Capture emails and use a follow-up sequence to educate and tell stories to those who were not convinced by the website. Done correctly, this can produce 2x to 3x as many sales as the website.

Simple, but not easy. It takes diligence and discipline to do these things well. But if you do, you will be richly rewarded!

And if you’d like to learn more about conversion rate optimization or review additional case studies, we encourage you to take our free course.

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My name is Patrick Curtis, and I’m the founder and CEO of Wall Street Oasis, an online community focused on careers in finance founded in 2006 with over 2 million visits per month.

User-generated content and long-tail organic traffic is what has built our business and community over the last 12+ years. But what happens if you wake up one day and realize that your growth has suddenly stopped? This is what happened to us back in November 2012.

In this case study, I’ll highlight two of our main SEO problems as a large forum with over 200,000 URLs, then describe two solutions that finally helped us regain our growth trajectory — almost five years later.

Two main problems

1. Algorithm change impacts

Ever since November 2012, Google’s algo changes have seemed to hurt many online forums like ours. Even though our traffic didn’t decline, our growth dropped to the single-digit percentages. No matter what we tried, we couldn’t break through our “plateau of pain” (I call it that because it was a painful ~5 years trying).

2. Quality of user-generated content

Related to the first problem, 99% of our content is user-generated (UGC) which means the quality is mixed (to put it kindly). Like most forum-based sites, some of our members create incredible pieces of content, but a meaningful percentage of our content is also admittedly thin and/or low-quality.

How could we deal with over 200,000 pieces of content efficiently and try to optimize them without going bankrupt? How could we “clean the cruft” when there was just so much of it?

Fighting back: Two solutions (and one statistical analysis to show how it worked)

1. “Merge and Purge” project

Our goal was to consolidate weaker “children” URLs into stronger “master” URLs to utilize some of the valuable content Google was ignoring and to make the user experience better.

For example, instead of having ~20 discussions on a specific topic (each with an average of around two to three comments) across twelve years, we would consolidate many of those discussions into the strongest two or three URLs (each with around 20–30 comments), leading to a much better user experience with less need to search and jump around the site.

Changes included taking the original post and comments from a “child” URL and merging them into the “master” URL, unpublishing the child URL, removing the child from sitemap, and adding a 301 redirect to the master.

Below is an example of how it looked when we merged a child into our popular Why Investment Banking discussion. We highlighted the original child post as a Related Topic with a blue border and included the original post date to help avoid confusion:

This was a massive project that involved some complex Excel sorting, but after 18 months and about $ 50,000 invested (27,418 children merged into 8,515 masters to date), the user experience, site architecture, and organization is much better.

Initial analysis suggests that the percentage gain from merging weak children URLs into stronger masters has given us a boost of ~10–15% in organic search traffic.

2. The Content Optimization Team

The goal of this initiative was to take the top landing pages that already existed on Wall Street Oasis and make sure that they were both higher quality and optimized for SEO. What does that mean, exactly, and how did we execute it?

We needed a dedicated team that had some baseline industry knowledge. To that end, we formed a team of five interns from the community, due to the fact that they were familiar with the common topics.

We looked at the top ~200 URLs over the previous 90 days (by organic landing page traffic) and listed them out in a spreadsheet:

We held five main hypotheses of what we believed would boost organic traffic before we started this project:

Longer content with subtitles: Increasing the length of the content and adding relevant H2 and H3 subtitles to give the reader more detailed and useful information in an organized fashion.

Changing the H1 so that it matched more high-volume keywords using Moz’s Keyword Explorer.

Changing the URL so that it also was a better match to high-volume and relevant keywords.

Adding a relevant image or graphic to help break up large “walls of text” and enrich the content.

Adding a relevant video similar to the graphic, but also to help increase time on page and enrich the content around the topic.

We tracked all five of these changes across all 200 URLs (see image above). After a statistical analysis, we learned that four of them helped our organic search traffic and one actually hurt.

Summary of results from our statistical analysis

Increasing the length of the articles and adding relevant subtitles (H2s, H3s, and H4s) to help organize the content gives an average boost to organic traffic of 14%

Improving the title or H1 of the URLs yields a 9% increase on average

Changing the URL decreased traffic on average by 38% (this was a smaller sample size — we stopped doing this early on for obvious reasons)

Including a relevant video increases the organic traffic by 4% on average, while putting an image up increases it by 5% on average.

Overall, the boost to organic traffic — should we continue to make these four changes (and avoid changing the URL) — is 32% on average.

Key takeaway:

Over half of that gain (~18%) comes from changes that require a minimal investment of time. For teams trying to optimize on-page SEO across a large number of pages, we recommend focusing on the top landing pages first and easy wins before deciding if further investment is warranted.

We hope this case study of our on-page SEO efforts was interesting, and I’m happy to answer any questions you have in the comments!

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What does Google consider “quality content”? And how do you capitalize on a seemingly subjective characteristic to improve your standing in search?

We’ve been trying to figure this out since the Hummingbird algorithm was dropped in our laps in 2013, prioritizing “context” over “keyword usage/frequency.” This meant that Google’s algorithm intended to understand the meaning behind the words on the page, rather than the page’s keywords and metadata alone.

This new sea change meant the algorithm was going to read in between the lines in order to deliver content that matched the true intent of someone searching for a keyword.

Write longer content? Not so fast!

Watching us SEOs respond to Google updates is hilarious. We’re like a floor full of day traders getting news on the latest cryptocurrency.

One of the most prominent theories that made the rounds was that longer content was the key to organic ranking. I’m sure you’ve read plenty of articles on this. We at Brafton, a content marketing agency, latched onto that one for a while as well. We even experienced some mixed success.

However, what we didn’t realize was that when we experienced success, it was because we accidentally stumbled on the true ranking factor.

Longer content alone was not the intent behind Hummingbird.

Content depth

Let’s take a hypothetical scenario.

If you were to search the keyword “search optimization techniques,” you would see a SERP that looks similar to the following:

Nothing too surprising about these results.

However, if you were to go through each of these 10 results and take note of the major topics they discussed, theoretically you would have a list of all the topics being discussed by all of the top ranking sites.

Example:

Position 1 topics discussed: A, C, D, E, F

Position 2 topics discussed: A, B, F

Position 3 topics discussed: C, D, F

Position 4 topics discussed: A, E, F

Once you finished this exercise, you would have a comprehensive list of every topic discussed (A–F), and you would start to see patterns of priority emerge.

In the example above, note “topic F” is discussed in all four pieces of content. One would consider this a cornerstone topic that should be prioritized.

If you were then to write a piece of content that covered each of the topics discussed by every competitor on page one, and emphasized the cornerstone topics appropriately, in theory, you would have the most comprehensive piece of content on that particular topic.

By producing the most comprehensive piece of content available, you would have the highest quality result that will best satisfy the searcher’s intent. More than that, you would have essentially created the ultimate resource center for everything a person would want to know about that topic.

How to identify topics to discuss in a piece of content

At this point, we’re only theoretical. The theory makes logical sense, but does it actually work? And how do we go about scientifically gathering information on topics to discuss in a piece of content?

Finding topics to cover:

Manually: As discussed previously, you can do it manually. This process is tedious and labor-intensive, but it can be done on a small scale.

Using SEMrush: SEMrush features an SEO content template that will provide guidance on topic selection for a given keyword.

Using MarketMuse:MarketMuse was originally built for the very purpose of content depth, with an algorithm that mimics Hummingbird. MM takes a largely unscientific process and makes it scientific. For the purpose of this case study, we used MarketMuse.

The process

Watch the process in action

1. Identify content worth optimizing

We went through a massive list of keywords our blog ranked for. We filtered that list down to keywords that were not ranking number one in SERPs but had strong intent. You can also do this with core landing pages.

Here’s an example: We were ranking in the third position for the keyword “financial content marketing.” While this is a low-volume keyword, we were enthusiastic to own it due to the high commercial intent it comes with.

2. Evaluate your existing piece

Take a subjective look at your piece of content that is ranking for the keyword. Does it SEEM like a comprehensive piece? Could it benefit from updated examples? Could it benefit from better/updated inline embedded media? With a cursory look at our existing content, it was clear that the examples we used were old, as was the branding.

3. Identify topics

As mentioned earlier, you can do this in a few different ways. We used MarketMuse to identify the topics we were doing a good job of covering as well as our topic gaps, topics that competitors were discussing, but we were not. The results were as follows:

Topics we did a good job of covering:

Content marketing impact on branding

Impact of using case studies

Importance of infographics

Business implications of a content marketing program

Creating articles for your audience

Topics we did a poor job of covering:

Marketing to millennials

How to market to existing clients

Crafting a content marketing strategy

Identifying and tracking goals

4. Rewrite the piece

Considering how out-of-date our examples were, and the number of topics we had neglected to discuss, we determined a full rewrite of the piece was warranted. Our writer, Mike O’Neill, was given the topic guidance, ensuring he had a firm understanding of everything that needed to be discussed in order to create a comprehensive article.

5. Update the content

To maintain our link equity, we kept the same URL and simply updated the old content with the new. Then we updated the publish date. The new article looks like this, with updated content depth, modern branding, and inline visuals.

6. Fetch as Google

Rather than wait for Google to reindex the content, I wanted to see the results immediately (and it is indeed immediate).

7. Check your results

Open an incognito window and see your updated position.

Promising results:

We have run more than a dozen experiments and have seen positive results across the board. As demonstrated in the video, these results are usually realized within 60 seconds of reindexing the updated content.

Keyword target

Old Ranking

New ranking

“Financial content marketing”

3

1

“What is a subdomain”

16

6

“Best company newsletters”

32

4

“Staffing marketing”

7

3

“Content marketing agency”

16

1

“Google local business cards”

16

5

“Company blog”

7

4

“SEO marketing tools”

9

3

Of those tests, here’s another example of this process in action for the keyword, “best company newsletters.”

Before:

After

Assumptions:

From these results, we can assume that content depth and breadth of topic coverage matters — a lot. Google’s algorithm seems to have an understanding of the competitive topic landscape for a keyword. In our hypothetical example from before, it would appear the algorithm knows that topics A–F exist for a given keyword and uses that collection of topics as a benchmark for content depth across competitors.

We can also assume Google’s algorithm either a.) responds immediately to updated information, or b.) has a cached snapshot of the competitive content depth landscape for any given keyword. Either of these scenarios is very likely because of the speed at which updated content is re-ranked.

In conclusion, don’t arbitrarily write long content and call it “high quality.” Choose a keyword you want to rank for and create a comprehensive piece of content that fully supports that keyword. There is no guarantee you’ll be granted a top position — domain strength factors play a huge role in rankings — but you’ll certainly improve your odds, as we have seen.

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As someone who has worked in Q&A forums for the majority of my digital marketing life, I took an immediate shine to the idea of Google Questions and Answers. Here’s a chance, I thought, for consumers and brands to take meaningful communication to a whole new level, exchanging requests, advice, and help so effortlessly. Here’s an opportunity for businesses to place answers to FAQs right upfront in the SERPs, while also capturing new data about consumer needs and desires. So cool!

But, so far, we seem to be getting off to a slow start. According to a recent, wide-scale GetFiveStars study, 25% of businesses now have questions waiting for them. I decided to hone in on San Francisco and look at 20 busy industries in that city to find out not just how many questions were being asked, but also how many answers were being given, and who was doing the answering. I broke down responders into three groups: Local Guides (LGs), random users (RUs), and owners (Os). I looked at the top 10 businesses ranking in the local finder for each industry:

Industry

Number of Questions

Number of Answers

LGs

RUs

Os

Dentists

1

0

0

0

0

Plumbers

2

0

-

-

-

Chiropractors

0

-

-

-

-

Mexican Restaurants

10

23

22

1

-

Italian Restaurants

15

20

19

1

-

Chinese Restaurants

16

53

49

4

-

Car Dealers

4

5

3

2

-

Supermarkets

7

27

24

3

-

Clothing Stores

4

1

1

-

-

Florists

1

0

-

-

-

Hotels

44

142

114

28

-

Real Estate Agencies

0

-

-

-

-

General Contractors

1

0

-

-

-

Cell Phone Stores

14

3

3

-

-

Yoga Studios

1

0

-

-

-

Banks

1

0

-

-

-

Carpet Cleaning

0

-

-

-

-

Hair Salons

1

0

-

-

-

Locksmiths

1

0

-

-

-

Jewelry Stores

0

-

-

-

-

Takeaways from the case study

Here are some patterns and oddities I noticed from looking at 123 questions and 274 answers:

There are more than twice as many answers as questions. While many questions received no answers, others received five, ten, or more.

The Owners column is completely blank. The local businesses I looked at in San Francisco are investing zero effort in answering Google Questions and Answers.

Unfortunately, what I’m seeing in Google Questions and Answers is that incentivizing replies is leading to a knowledge base of questionable quality. How helpful is it when a consumer asks a hotel if they have in-room hair dryers and 10 local guides jump on the bandwagon with “yep”? Worse yet, I saw quite a few local guides replying “I don’t know,” “maybe,” and even “you should call the business and ask.” Here and there, I saw genuinely helpful answers from the Local Guides, but my overall impression didn’t leave me feeling like I’d stumbled upon a new Google resource of matchless expertise.

Some members of the public seem to be confused about the use of this feature. I noticed people using the answer portion to thank people who replied to their query, rather than simply using the thumbs up widget.

Additionally, I saw people leaving reviews/statements, instead of questions:And with a touch of exasperated irony:And to rant:

Some industries are clearly generating far more questions than others. Given how people love to talk about hotels and restaurants, I wasn’t surprised to see them topping the charts in sheer volume of questions and answers. What did surprise me was not seeing more questions being asked of businesses like yoga studios, florists, and hair salons; before I actually did the searches, I might have guessed that pleasant, “chatty” places like these would be receiving lots of queries.

I chose San Francisco for my case study because of its general reputation for being hip to new tech, but just in case my limited focus was presenting a false picture of how local businesses are managing this feature, I did some random searches for big brands around the state and around the country.

But the hands-down winner for a single location lacking official answers is Google in Mountain View. 103 questions as of my lookup and nary an owner answer in sight. Alphabet might want to consider setting a more inspiring example with their own product… unless I’m misunderstanding their vision of how Google Questions and Answers is destined to be used.

Just what is the vision for Google Questions and Answers, I wonder?

As I said at the beginning of this post, it’s early days yet to predict ultimate outcomes. Yet, the current lay of the land for this feature has left me with more questions than answers:

Does Google actually intend questions to be answered by brands, or by the public? From what I’ve seen, owners are largely unaware of or choosing to ignore this feature many months post-launch. As of writing this, businesses are only alerted about incoming questions if they open the Google Maps app on an Android phone or tablet. There is no desktop GMB dashboard section for the feature. It’s not a recipe for wide adoption. Google has always been a fan of a crowdsourcing approach to their data, so they may not be concerned, but that doesn’t mean your business shouldn’t be.

What are the real-time expectations for this feature? I see many users asking questions that needed fast answers, like “are you open now?” while others might support lengthier response times, as in, “I’m planning a trip and want to know what I can walk to from your hotel.” For time-sensitive queries, how does Questions and Answers fit in with Google’s actual chat feature, Google Messaging, also rolled out last summer? Does Google envision different use cases for both features? I wonder if one of the two products will win out over time, while the other gets sunsetted.

What are the real, current risks to brands of non-management? I applauded Mike Blumenthal’s smart suggestion of companies proactively populating the feature with known FAQs and providing expert answers, and I can also see the obvious potential for reputation damage if rants or spam are ignored. That being said, my limited exploration of San Francisco has left me wondering just how many people (companies or consumers) are actually paying attention in most industries. Google Knowledge Panels and the Local Finder pop-ups are nearing an information bloat point. Do you want to book something, look at reviews, live chat, see menus, find deals, get driving directions, make a call? Websites are built with multiple pages to cover all of these possible actions. Sticking them all in a 1” box may not equal the best UX I’ve ever seen, if discovery of features is our goal.

What is the motivation for consumers to use the product? Personally, I’d be more inclined to just pick up the phone to ask any question to which I need a fast answer. I don’t have the confidence that if I queried Whole Foods in the AM as to whether they’ve gotten in organic avocados from California, there’d be a knowledge panel answer in time for my lunch. Further, some of the questions I’ve asked have received useless answers from the public, which seems like a waste of time for all parties. Maybe if the feature picks up momentum, this will change.

Will increasing rates of questions = increasing rates of business responses? According to the GetFiveStars study linked to above, total numbers of questions for the 1700 locations they investigated nearly doubled between November–December of 2017. From my microscopic view of San Francisco, it doesn’t appear to me that the doubling effect also happened for owner answers. Time will tell, but for now, what I’m looking for is question volume reaching such a boiling point that owners feel obligated to jump into management, as they have with reviews. We’re not there yet, but if this feature is a Google keeper, we could get there.

So what should you be doing about Google Questions and Answers?

I’m a fan of early adoption where it makes sense. Speculatively, having an active Questions and Answers presence could end up as a ranking signal. We’ve already seen it theorized that use of another Google asset, Google Posts, may impact local pack rankings. Unquestionably, leaving it up to the public to answer questions about your business with varying degrees of accuracy carries the risk of losing leads and muddying your online presence to the detriment of reputation. If a customer asks if your location has wheelchair access and an unmotivated third party says “I don’t know,” when, in fact, your business is fully ADA-compliant, your lack of an answer becomes negative customer service. Because of this, ignoring the feature isn’t really an option. And, while I wouldn’t prioritize management of Questions and Answers over traditional Google-based reviews at this point, I would suggest:

Do a branded search today and look at your knowledge panel to see if you’ve received any questions. If so, answer them in your best style, as helpfully as possible

Spend half an hour this week translating your company’s 5 most common FAQs into Google Questions and Answers queries and then answering them. Be sure you’re logged into your company’s Google account when you reply, so that your message will be officially stamped with the word “owner.” Whether you proactively post your FAQs while logged into your business’ account is up to you. I think it’s more transparent to do so.

If you’re finding this part of your Knowledge Panel isn’t getting any questions, checking it once a week is likely going to be enough for the present.

If you happen to be marketing a business that is seeing some good Questions and Answers activity, and you have the bandwidth, I’d add checking this to the daily social media rounds you make for the purpose of reputation management. I would predict that if Google determines this feature is a keeper, they’ll eventually start sending email alerts when new queries come in, as they’re now doing with reviews, which should make things easier and minimize the risk of losing a customer with an immediate need. Need to go pro on management right now due to question volume? GetFiveStars just launched an incredibly useful Google Q&A monitoring feature, included in some of their ORM software packages. Looks like a winner!

Do be on the lookout for spam inquiries and responses, and report them if they arise.

If you’re totally new to Google Questions and Answers, this simple infographic will get you going in a flash:

My questions, your answers

My case study is small. Can you help expand our industry’s knowledge base by answering a few questions in the comments to add to the picture of the current rate of adoption/usefulness of Google’s Questions and Answers? Please, let me know:

Have you asked a question using this feature?

Did you receive an answer and was it helpful?

Who answered? The business, a random user, a Local Guide?

Have you come across any examples of business owners doing a good job answering questions?

What are your thoughts on Google Questions and Answers? Is it a winner? Worth your time? Any tips?

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